Bibliographic record
Abstract
The Kepler mission discovered thousands of exoplanets and completely revolutionised planetary astronomy. Over one thousand of these exoplanets are in multi-planet systems with architectures remarkably different from that of our Solar System: compact, multi-planet systems. Many hypotheses about their evolution involve dynamical sculpting --- gravitational planet-planet interactions accumulating over billions of orbits. This can drastically alter their orbits, consequently sculpting multi-planet systems into the architectures of the observed, mature Kepler systems. In this dissertation, I explore dynamical sculpting through two different projects. First, I examine how the dynamical spacing between orbits affects the system's instability timescale. Probing a high-density distribution in dynamical spacing, I saw additional structure on top of the instability-spacing relationship which corresponded to period commensurabilities. Consequently, I hypothesise that interacting mean motion resonances of multiple pairs of planets are responsible for the dynamics of such systems, and ultimately driving the instability. Second, I re-visit the dynamical packing of the Kepler multi-planet systems. Using a machine learning method to examine the stability of over 9 million different orbital configurations, I found that most systems are strongly packed. Furthermore, dynamical packing increases with a system's observed planet multiplicity, which is consistent with dynamical sculpting throughout system evolution or with lower multiplicity systems having a higher likelihood of unseen planets. These projects advance the field's knowledge of the dynamics of compact, multi-planet systems and the role of dynamical sculpting throughout their evolution. My first project provided a foundation to further understand the dynamics and instability which are responsible for dynamical sculpting. My second project quantified a dynamical property of mature, observed systems, finding that it may be consistent with such systems being dynamically sculpted. The road ahead for research in this field is promising --- current and upcoming surveys will expand the catalogue of observed multi-planet systems and machine learning methods will continue to improve our theoretical understanding of planetary dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".